Contradiction Resolution of Competitive and Input Neurons to Improve Prediction and Visualization Performance
Contradiction Resolution of Competitive and Input Neurons to Improve Prediction and Visualization Performance
复制标题
DOI：
10.14569/ijarai.2013.021206
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
R. Kamimura
中科院分区：
文献类型：
--
作者：
R. Kamimura
在本文中，我们提出了一种新的类型的信息论方法来解决竞争和输入神经元中观察到的矛盾。对于竞争神经元，存在着自评价（个体性）和外评价（集体性）的矛盾，这种矛盾被简化为实现自组织映射。对于输入神经元，存在使用多个输入神经元和使用少个输入神经元之间的矛盾。我们试图实现一种情况，即使用尽可能多的输入神经元，同时只使用少数输入神经元。这种矛盾的情况可以通过在不同的水平上观察输入神经元来解决，即个体和平均水平。我们将矛盾解决应用于两个数据集，即日本短期经济调查（短观）和美元-日元汇率。在这两个数据集中，我们成功地提高了预测性能。平均使用许多输入神经元，但对于每个输入模式仅采用少数输入神经元。此外，连接权重被压缩成少量的不同的组，以获得更好的预测和解释性能。
In this paper, we propose a new type of informationtheoretic method to resolve the contradiction observed in competitive and input neurons. For competitive neurons, contradiction between self-evaluation (individuality) and outer-evaluation (collectivity) exists, which is reduced to realize the self-organizing maps. For input neurons, there exists contradiction between the use of many and few input neurons. We try to realize a situation where as many input neurons as possible are used, and at the same time, another where only a few input neurons are used. This contradictory situation can be resolved by viewing input neurons on different levels, namely, the individual and average level. We applied contradiction resolution to two data sets, namely, the Japanese short term economy survey (Tankan) and Dollar-Yen exchange rates. In both data sets, we succeeded in improving the prediction performance. Many input neurons were used on average, but a few input neurons were only taken for each input pattern. In addition, connection weights were condensed into a small number of distinct groups for better prediction and interpretation performance.